Removing Silent Data Truncation: How Hard-Coded Limits Break Scalability

Key Takeaway

We discovered a hard-coded 3,000 annotation limit in our backend that silently truncated user data without warning. Removing this arbitrary constraint enabled our system to scale from thousands to unlimited annotations, preventing data loss and improving user trust.

The Problem

Our annotation repository was imposing an artificial ceiling on data retrieval. When users tried to view or process projects with more than 3,000 annotations, the system would only return the first 3,000 records—without any error message or warning. This created several critical issues:

1. Silent Data Loss: Users had no idea their data was being truncated
2. Inconsistent Results: Analyses and visualizations showed incomplete data
3. Scalability Barrier: Projects couldn't grow beyond arbitrary limits
4. Trust Erosion: Users questioned system reliability when data disappeared
5. Support Burden: Investigation of "missing data" consumed engineering time

Context and Background

Our system manages spatial image annotations for AI model training and analysis. Medical imaging projects routinely generate tens of thousands of annotations per slide. The hard-coded limit appeared in two critical locations:

# In AnnotationRepository
def get_all_annotations(self, project_id):
    return db.session.query(Annotation)\
        .filter(Annotation.project_id == project_id)\
        .limit(3000)\  # Hard-coded limit
        .all()

# In AnnotationQuery
class AnnotationQuery:
    MAX_RECORDS = 3000  # Arbitrary constant

This limit was likely added early in development as a safety measure but was never removed or made configurable. As our platform matured and users began working with production-scale datasets, the limitation became a critical bottleneck.

The Solution

We removed the hard-coded limits entirely and let the database handle record retrieval naturally:

# Fixed version - no artificial limits
def get_all_annotations(self, project_id):
    return db.session.query(Annotation)\
        .filter(Annotation.project_id == project_id)\
        .all()  # Database handles all records

We also implemented pagination for large result sets to maintain performance:

def get_annotations_paginated(self, project_id, page=1, per_page=5000):
    return db.session.query(Annotation)\
        .filter(Annotation.project_id == project_id)\
        .offset((page - 1) * per_page)\
        .limit(per_page)\
        .all()

Implementation Details

The fix involved:

1. Code Review: Identified all locations with hard-coded limits
2. Database Analysis: Verified query performance without limits
3. Pagination Strategy: Added batch processing for memory efficiency
4. Migration Path: Ensured existing queries continued working
5. Testing: Validated with projects containing 50,000+ annotations

Impact and Results

After removing the hard-coded limits:

• Data Integrity: All annotations now accessible without truncation
• Scale Achievement: Successfully processed projects with 100,000+ annotations
• Performance: No degradation when combined with pagination
• User Confidence: Eliminated "missing data" support tickets
• Future-Proof: System now scales naturally with user needs

Lessons Learned

1. Question Magic Numbers: Hard-coded limits should always be configurable or removed
2. Fail Loudly: If limits exist, warn users when approaching them
3. Test at Scale: Validate system behavior with production-scale data
4. Pagination First: Design for large datasets from the beginning
5. Remove Legacy Constraints: Regularly review code for outdated safety measures

Hard-coded limits are technical debt that accumulates silently. Regular code audits help identify and eliminate these constraints before they impact users.